Slowdown detection

1. A method involving a computing device comprising a processor, and the method comprising:

executing, on the processor, instructions that cause the computing device to perform operations, the operations comprising:

evaluating location data received from vehicles traveling a road to identify a road segment;

generating a space-time diagram, comprising a first axis representing distance along the road segment and a second axis representing time, based upon the location data associated with the vehicles traveling the road segment; and

processing the space-time diagram using a neural network trained to utilize image recognition functionality to detect whether space-time diagrams illustrate slowdowns of the vehicles, wherein the neural network is executed to construct a time series of transition points based upon features extracted from the space-time diagram.

2. The method of claim 1 , the operations comprising:

transmitting a notification of a slowdown, identified from the space-time diagram, to a computing device associated with a driver of a vehicle that is to travel the road segment.

3. The method of claim 2 , comprising:

constructing the notification to comprise a location of a transition point between free flowing vehicle traffic and congested flow vehicle traffic along the road segment due to the slowdown.

4. The method of claim 2 , comprising:

evaluating the space-time diagram and vehicle speed data to predict a time frame of the slowdown dissipating; and

constructing the notification to comprise the time frame.

5. The method of claim 2 , comprising:

evaluating the space-time diagram and vehicle speed data to predict a future location of a transition point between free flowing vehicle traffic and congested flow vehicle traffic along the road segment due to the slowdown; and

constructing the notification to comprise the future location.

6. The method of claim 2 , comprising:

constructing the notification to comprise a timeframe of the slowdown.

7. The method of claim 1 , the operations comprising:

assigning colors to vehicle trajectories within the space-time diagram, wherein each color corresponds to a different range of vehicle speeds.

8. The method of claim 1 , comprising:

applying a Kalman filter to determine a transition point between free flowing vehicle traffic and congested flow vehicle traffic along the road segment due to a slowdown and predict a future location of the transition point.

9. The method of claim 8 , comprising:

separating vehicle speeds into separate clusters using the transition point;

determining a difference between a first median of a first cluster representing free flow traffic speeds and a second median of a second cluster representing slowdown traffic speeds; and

identifying the slowdown based upon the difference between the first median and the second median exceeding a speed threshold.

10. The method of claim 9 , comprising:

evaluating the first cluster and the second cluster to identify the transition point where a back of queue of the slowdown is located along the road segment.

11. The method of claim 9 , comprising:

applying a Hampel filter to the first cluster and the second cluster to remove outliers vehicle speeds.

12. A computing device comprising:

a processor; and

memory comprising processor-executable instructions that when executed by the processor cause performance of operations, the operations comprising:

evaluating location data received from vehicles traveling a road to identify a road segment;

generating a space-time diagram, comprising a first axis representing distance along the road segment and a second axis representing time, based upon the location data associated with the vehicles traveling the road segment; and

processing the space-time diagram using a neural network trained to utilize image recognition functionality to detect whether space-time diagrams illustrate slowdowns of the vehicles, wherein the neural network is executed to construct a time series of transition points based upon features extracted from the space-time diagram.

13. The computing device of claim 12 , the operations comprising:

constructing a notification to comprise a location of a transition point between free flowing vehicle traffic and congested flow vehicle traffic along the road segment due to a slowdown, wherein the transition point along the road segment is converted into longitudinal and latitude values to identify the location.

14. The computing device of claim 12 , the operations comprising:

identifying at least one of a road condition or a weather condition for the road segment; and

factoring in the least one of the road condition or the weather condition when determining whether there is a slowdown along the road segment.

15. The computing device of claim 12 , the operations comprising:

tracking occurrences of the slowdowns for the road segment as historical data; and

factoring in the historical data when determining whether there is a slowdown along the road segment.

16. The computing device of claim 12 , the operations comprising:

transmitting a notification to computing devices associated with vehicles within a threshold distance of a location of a slowdown.

17. A non-transitory machine readable medium having stored thereon processor-executable instructions that when executed cause performance of operations, the operations comprising:

evaluating location data received from vehicles traveling a road to identify a road segment;

generating a space-time diagram, comprising a first axis representing distance along the road segment and a second axis representing time, based upon the location data associated with the vehicles traveling the road segment; and

processing the space-time diagram using a neural network trained to utilize image recognition functionality to detect whether space-time diagrams illustrate slowdowns of the vehicles, wherein the neural network is executed to construct a time series of transition points based upon features extracted from the space-time diagram.

18. The non-transitory machine readable medium of claim 17 , the operations comprising:

transmitting a notification to computing devices associated with vehicles that are traveling routes that will encounter a slowdown during a predicted duration of the slowdown.

19. The non-transitory machine readable medium of claim 17 , the operations comprising:

clustering vehicle speeds represented within the space-time diagram using a clustering algorithm;

determining a difference between a first median of a first cluster representing free flow traffic speeds and a second median of a second cluster representing slowdown traffic speeds; and

identifying a slowdown based upon the difference exceeding a speed threshold.

20. The non-transitory machine readable medium of claim 19 , the operations comprising:

evaluating the first cluster and the second cluster to identify a transition point where a back of queue of the slowdown is located along the road segment.